IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v449y2021ics0304380021000727.html
   My bibliography  Save this article

Which spatial interpolators I should use? A case study applying to marine species

Author

Listed:
  • Rufino, Marta M.
  • Albouy, Camille
  • Brind'Amour, Anik

Abstract

Species are spread in space, whereas sampling is sparse. Thus, to describe and map along environmental gradients, it is necessary to interpolate the species abundance. Considering the plethora of valid methods, the researcher gets easily puzzled to choose the most appropriate interpolation approach with reference to the ecological question being asked.

Suggested Citation

  • Rufino, Marta M. & Albouy, Camille & Brind'Amour, Anik, 2021. "Which spatial interpolators I should use? A case study applying to marine species," Ecological Modelling, Elsevier, vol. 449(C).
    • Handle: RePEc:eee:ecomod:v:449:y:2021:i:c:s0304380021000727
    DOI: 10.1016/j.ecolmodel.2021.109501
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380021000727
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2021.109501?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jin Li, 2017. "Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what?," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    2. Tomislav Hengl & Gerard B M Heuvelink & Bas Kempen & Johan G B Leenaars & Markus G Walsh & Keith D Shepherd & Andrew Sila & Robert A MacMillan & Jorge Mendes de Jesus & Lulseged Tamene & Jérôme E Tond, 2015. "Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-26, June.
    3. Nicole H. Augustin & Verena M. Trenkel & Simon N. Wood & Pascal Lorance, 2013. "Space‐time modelling of blue ling for fisheries stock management," Environmetrics, John Wiley & Sons, Ltd., vol. 24(2), pages 109-119, March.
    4. Marta Mega Rufino & Nicolas Bez & Anik Brind’Amour, 2018. "Integrating spatial indicators in the surveillance of exploited marine ecosystems," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-21, November.
    5. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.
    2. Václavík, Tomáš & Meentemeyer, Ross K., 2009. "Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?," Ecological Modelling, Elsevier, vol. 220(23), pages 3248-3258.
    3. Muñoz-Mas, Rafael & Vezza, Paolo & Alcaraz-Hernández, Juan Diego & Martínez-Capel, Francisco, 2016. "Risk of invasion predicted with support vector machines: A case study on northern pike (Esox Lucius, L.) and bleak (Alburnus alburnus, L.)," Ecological Modelling, Elsevier, vol. 342(C), pages 123-134.
    4. Sabastine Ugbemuna Ugbaje & Thomas F.A. Bishop, 2020. "Hydrological Control of Vegetation Greenness Dynamics in Africa: A Multivariate Analysis Using Satellite Observed Soil Moisture, Terrestrial Water Storage and Precipitation," Land, MDPI, vol. 9(1), pages 1-15, January.
    5. Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
    6. Marmion, Mathieu & Luoto, Miska & Heikkinen, Risto K. & Thuiller, Wilfried, 2009. "The performance of state-of-the-art modelling techniques depends on geographical distribution of species," Ecological Modelling, Elsevier, vol. 220(24), pages 3512-3520.
    7. Kaiping Wang & Weiqi Wang & Niyi Zha & Yue Feng & Chenlan Qiu & Yunlu Zhang & Jia Ma & Rui Zhang, 2022. "Spatially Heterogeneity Response of Critical Ecosystem Service Capacity to Address Regional Development Risks to Rapid Urbanization: The Case of Beijing-Tianjin-Hebei Urban Agglomeration in China," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    8. Joachim Eisenberg & Fabrice A. Muvundja, 2020. "Quantification of Erosion in Selected Catchment Areas of the Ruzizi River (DRC) Using the (R)USLE Model," Land, MDPI, vol. 9(4), pages 1-18, April.
    9. Chantal M. J. Hendriks & Harry S. Gibson & Anna Trett & André Python & Daniel J. Weiss & Anton Vrieling & Michael Coleman & Peter W. Gething & Penny A. Hancock & Catherine L. Moyes, 2019. "Mapping Geospatial Processes Affecting the Environmental Fate of Agricultural Pesticides in Africa," IJERPH, MDPI, vol. 16(19), pages 1-22, September.
    10. Aertsen, Wim & Kint, Vincent & van Orshoven, Jos & Özkan, Kürşad & Muys, Bart, 2010. "Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests," Ecological Modelling, Elsevier, vol. 221(8), pages 1119-1130.
    11. Ravic Nijbroek & Kristin Piikki & Mats Söderström & Bas Kempen & Katrine G. Turner & Simeon Hengari & John Mutua, 2018. "Soil Organic Carbon Baselines for Land Degradation Neutrality: Map Accuracy and Cost Tradeoffs with Respect to Complexity in Otjozondjupa, Namibia," Sustainability, MDPI, vol. 10(5), pages 1-20, May.
    12. Rogna, Marco, 2023. "The Effects of Rising Prices on Corn Production in Western African Countries," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK 334549, Agricultural Economics Society - AES.
    13. Berazneva, Julia & McBride, Linden & Sheahan, Megan & Guerena, David, 2016. "Perceived, measured, and estimated soil fertility in east Africa: Implications for farmers and researchers," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235466, Agricultural and Applied Economics Association.
    14. Akpoti, Komlavi & Groen, Thomas & Dossou-Yovo, Elliott & Kabo-bah, Amos T. & Zwart, Sander J., 2022. "Climate change-induced reduction in agricultural land suitability of West-Africa's inland valley landscapes," Agricultural Systems, Elsevier, vol. 200(C).
    15. Stoklosa, Jakub & Huang, Yih-Huei & Furlan, Elise & Hwang, Wen-Han, 2016. "On quadratic logistic regression models when predictor variables are subject to measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 109-121.
    16. Amirhossein Hassani & Adisa Azapagic & Nima Shokri, 2021. "Global predictions of primary soil salinization under changing climate in the 21st century," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    17. Amit Kumar & Pravash Chandra Moharana & Roomesh Kumar Jena & Sandeep Kumar Malyan & Gulshan Kumar Sharma & Ram Kishor Fagodiya & Aftab Ahmad Shabnam & Dharmendra Kumar Jigyasu & Kasthala Mary Vijaya K, 2023. "Digital Mapping of Soil Organic Carbon Using Machine Learning Algorithms in the Upper Brahmaputra Valley of Northeastern India," Land, MDPI, vol. 12(10), pages 1-17, September.
    18. Moreno-Amat, Elena & Mateo, Rubén G. & Nieto-Lugilde, Diego & Morueta-Holme, Naia & Svenning, Jens-Christian & García-Amorena, Ignacio, 2015. "Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data," Ecological Modelling, Elsevier, vol. 312(C), pages 308-317.
    19. Falconnier, Gatien N. & Leroux, Louise & Beillouin, Damien & Corbeels, Marc & Hijmans, Robert J. & Bonilla-Cedrez, Camila & van Wijk, Mark & Descheemaeker, Katrien & Zingore, Shamie & Affholder, Franç, 2023. "Increased mineral fertilizer use on maize can improve both household food security and regional food production in East Africa," Agricultural Systems, Elsevier, vol. 205(C).
    20. Khalin, Andrey A. & Postnikov, Eugene B., 2020. "A wavelet-based approach to revealing the Tweedie distribution type in sparse data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:449:y:2021:i:c:s0304380021000727. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.